{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/noise-tolerant-paradigm-for-training-face","title":"Noise-Tolerant Paradigm for Training Face Recognition CNNs","arxiv_id":"1903.10357","date":"2019-03-25","proceeding":"CVPR 2019 6","authors":["Wei Hu","Yangyu Huang","Fan Zhang","Ruirui Li"],"abstract":"Benefit from large-scale training datasets, deep Convolutional Neural\nNetworks(CNNs) have achieved impressive results in face recognition(FR).\nHowever, tremendous scale of datasets inevitably lead to noisy data, which\nobviously reduce the performance of the trained CNN models. Kicking out wrong\nlabels from large-scale FR datasets is still very expensive, although some\ncleaning approaches are proposed. According to the analysis of the whole\nprocess of training CNN models supervised by angular margin based loss(AM-Loss)\nfunctions, we find that the $\\theta$ distribution of training samples\nimplicitly reflects their probability of being clean. Thus, we propose a novel\ntraining paradigm that employs the idea of weighting samples based on the above\nprobability. Without any prior knowledge of noise, we can train high\nperformance CNN models with large-scale FR datasets. Experiments demonstrate\nthe effectiveness of our training paradigm. The codes are available at\nhttps://github.com/huangyangyu/NoiseFace.","url_abs":"http://arxiv.org/abs/1903.10357v2","url_pdf":"http://arxiv.org/pdf/1903.10357v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"noise-tolerant-paradigm-for-training-face","repo_url":"https://github.com/huangyangyu/NoiseFace","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"noise-tolerant-paradigm-for-training-face","repo_url":"https://github.com/Eltomad/Noiseface","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"face-recognition","task_name":"Face Recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1903.10357","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}